https://store-images.s-microsoft.com/image/apps.32310.0fa90d12-3930-4e0a-83cb-0b5965aa6024.9949bb29-bc70-482a-ae11-7ae251608fff.059408b2-2bca-489f-9d74-b71ea11298d2
Tredence's ML Works, Machine Learning Operations (MLOps) Platform
Tredence Inc
Tredence's ML Works, Machine Learning Operations (MLOps) Platform
Tredence Inc
Tredence's ML Works, Machine Learning Operations (MLOps) Platform
Tredence Inc
Get end-to-end visibility into your ML Pipelines with minimal manual effort and Model Ops
MLOps addresses the challenges with model and data drift and ensures that, through monitoring, AI and ML models stay relevant. Tredence's MLWorks natively integrates with Microsoft Azure, ensuring scalability and efficiency to enable organizations of any size to deliver machine learning model deployment, monitoring, and testing capabilities.
Distinguishing features of MLWorks are:
- Visual provenance graph: an end-to-end model visibility and pipeline traceability graph, allowing easy troubleshooting of production issues and root cause analysis.
- Persona-based dashboards—relevant metrics for business users, data scientists, and ML/Data engineers—with a persona-based monitoring journey to meet the specific use cases of all personas
- Model and data drift analysis: analysis of data and model drift (overall and feature level) with auto-triggered alerts, enabling continuous monitoring of the performance of production models
- Lineage tracking and provenance Review dashboard metrics all the way back to base models, ensuring full visibility into model operations along with training data.
- Governance and Support: centralized access control, traceability, and audit logs to manage multiple user personas and ensure regulatory compliance across platforms; maintenance and uptime governance through an SLA-driven response and resolution process.
- Myriad business requirements are met by platform-agnostic advisory & development models using cross-platform and open-source setup and support models.
User Interactions
- Data Scientist: Statistical insights about the model's health, performance, and algorithm-level details
- Business users: understand different business problems and define specifications for data engineers.
- Data Engineer: Insights into system performance and identification of scope for improving the SLA and reducing downtime
Highlights:
- Intuitive MLOps Graph
- Persona Based Insights
- Insights into the model health for faster value realization
- Autonomous monitoring and 360-degree view of model management
- Holistic MLOps management and engagement mode
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